Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning

TL;DR

This paper critically examines the common practice of sampling with replacement in computing confidence intervals for few-shot learning, revealing underestimation issues and proposing strategies for more accurate evaluation.

Contribution

It highlights the problem of underestimating confidence intervals in FSL due to sampling methods and introduces improved evaluation techniques and a new benchmark.

Findings

Sampling with replacement underestimates CI in FSL

Paired tests can partially correct CI underestimation

Strategic task sampling reduces CI size

Abstract

The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i.e.\ allowing the same samples to appear in multiple tasks. This makes the CI misleading in that it takes into account the randomness of the sampler but not the data itself. To quantify the extent of this problem, we conduct a comparative analysis between CIs computed with and without replacement. These reveal a notable underestimation by the predominant method. This observation calls for a reevaluation of how we interpret confidence intervals and the resulting conclusions in FSL comparative studies. Our research demonstrates that the use of paired tests can partially address this issue. Additionally, we explore methods to further reduce the (size of the) CI by strategically sampling tasks of a specific size. We also introduce a new optimized benchmark, which can be accessed at https://github.com/RafLaf/FSL-benchmark-again